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test_new_kvstore.py
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import os
import time
import numpy as np
import socket
from scipy import sparse as spsp
import dgl
import backend as F
import unittest, pytest
from dgl.graph_index import create_graph_index
import multiprocessing as mp
from numpy.testing import assert_array_equal
if os.name != 'nt':
import fcntl
import struct
def get_local_usable_addr():
"""Get local usable IP and port
Returns
-------
str
IP address, e.g., '192.168.8.12:50051'
"""
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# doesn't even have to be reachable
sock.connect(('10.255.255.255', 1))
ip_addr = sock.getsockname()[0]
except ValueError:
ip_addr = '127.0.0.1'
finally:
sock.close()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("", 0))
sock.listen(1)
port = sock.getsockname()[1]
sock.close()
return ip_addr + ' ' + str(port)
def create_random_graph(n):
arr = (spsp.random(n, n, density=0.001, format='coo') != 0).astype(np.int64)
ig = create_graph_index(arr, readonly=True)
return dgl.DGLGraph(ig)
# Create an one-part Graph
node_map = F.tensor([0,0,0,0,0,0], F.int64)
edge_map = F.tensor([0,0,0,0,0,0,0], F.int64)
global_nid = F.tensor([0,1,2,3,4,5], F.int64)
global_eid = F.tensor([0,1,2,3,4,5,6], F.int64)
g = dgl.DGLGraph()
g.add_nodes(6)
g.add_edge(0, 1) # 0
g.add_edge(0, 2) # 1
g.add_edge(0, 3) # 2
g.add_edge(2, 3) # 3
g.add_edge(1, 1) # 4
g.add_edge(0, 4) # 5
g.add_edge(2, 5) # 6
g.ndata[dgl.NID] = global_nid
g.edata[dgl.EID] = global_eid
gpb = dgl.distributed.GraphPartitionBook(part_id=0,
num_parts=1,
node_map=node_map,
edge_map=edge_map,
part_graph=g)
node_policy = dgl.distributed.PartitionPolicy(policy_str='node',
part_id=0,
partition_book=gpb)
edge_policy = dgl.distributed.PartitionPolicy(policy_str='edge',
part_id=0,
partition_book=gpb)
data_0 = F.tensor([[1.,1.],[1.,1.],[1.,1.],[1.,1.],[1.,1.],[1.,1.]], F.float32)
data_0_1 = F.tensor([1.,2.,3.,4.,5.,6.], F.float32)
data_0_2 = F.tensor([1,2,3,4,5,6], F.int32)
data_0_3 = F.tensor([1,2,3,4,5,6], F.int64)
data_1 = F.tensor([[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.]], F.float32)
data_2 = F.tensor([[0.,0.],[0.,0.],[0.,0.],[0.,0.],[0.,0.],[0.,0.]], F.float32)
def init_zero_func(shape, dtype):
return F.zeros(shape, dtype, F.cpu())
def udf_push(target, name, id_tensor, data_tensor):
target[name] = F.scatter_row(target[name], id_tensor, data_tensor*data_tensor)
@unittest.skipIf(os.name == 'nt' or os.getenv('DGLBACKEND') == 'tensorflow', reason='Do not support windows and TF yet')
def test_partition_policy():
assert node_policy.policy_str == 'node'
assert edge_policy.policy_str == 'edge'
assert node_policy.part_id == 0
assert edge_policy.part_id == 0
local_nid = node_policy.to_local(F.tensor([0,1,2,3,4,5]))
local_eid = edge_policy.to_local(F.tensor([0,1,2,3,4,5,6]))
assert_array_equal(F.asnumpy(local_nid), F.asnumpy(F.tensor([0,1,2,3,4,5], F.int64)))
assert_array_equal(F.asnumpy(local_eid), F.asnumpy(F.tensor([0,1,2,3,4,5,6], F.int64)))
nid_partid = node_policy.to_partid(F.tensor([0,1,2,3,4,5], F.int64))
eid_partid = edge_policy.to_partid(F.tensor([0,1,2,3,4,5,6], F.int64))
assert_array_equal(F.asnumpy(nid_partid), F.asnumpy(F.tensor([0,0,0,0,0,0], F.int64)))
assert_array_equal(F.asnumpy(eid_partid), F.asnumpy(F.tensor([0,0,0,0,0,0,0], F.int64)))
assert node_policy.get_data_size() == len(node_map)
assert edge_policy.get_data_size() == len(edge_map)
def start_server():
# Init kvserver
kvserver = dgl.distributed.KVServer(server_id=0,
ip_config='kv_ip_config.txt',
num_clients=1)
kvserver.add_part_policy(node_policy)
kvserver.add_part_policy(edge_policy)
kvserver.init_data('data_0', 'node', data_0)
kvserver.init_data('data_0_1', 'node', data_0_1)
kvserver.init_data('data_0_2', 'node', data_0_2)
kvserver.init_data('data_0_3', 'node', data_0_3)
# start server
server_state = dgl.distributed.ServerState(kv_store=kvserver)
dgl.distributed.start_server(server_id=0,
ip_config='kv_ip_config.txt',
num_clients=1,
server_state=server_state)
def start_client():
# Note: connect to server first !
dgl.distributed.connect_to_server(ip_config='kv_ip_config.txt')
# Init kvclient
kvclient = dgl.distributed.KVClient(ip_config='kv_ip_config.txt')
kvclient.init_data(name='data_1',
shape=F.shape(data_1),
dtype=F.dtype(data_1),
policy_str='edge',
partition_book=gpb,
init_func=init_zero_func)
kvclient.init_data(name='data_2',
shape=F.shape(data_2),
dtype=F.dtype(data_2),
policy_str='node',
partition_book=gpb,
init_func=init_zero_func)
kvclient.map_shared_data(partition_book=gpb)
# Test data_name_list
name_list = kvclient.data_name_list()
print(name_list)
assert 'data_0' in name_list
assert 'data_0_1' in name_list
assert 'data_0_2' in name_list
assert 'data_0_3' in name_list
assert 'data_1' in name_list
assert 'data_2' in name_list
# Test get_meta_data
meta = kvclient.get_data_meta('data_0')
dtype, shape, policy = meta
assert dtype == F.dtype(data_0)
assert shape == F.shape(data_0)
assert policy.policy_str == 'node'
meta = kvclient.get_data_meta('data_0_1')
dtype, shape, policy = meta
assert dtype == F.dtype(data_0_1)
assert shape == F.shape(data_0_1)
assert policy.policy_str == 'node'
meta = kvclient.get_data_meta('data_0_2')
dtype, shape, policy = meta
assert dtype == F.dtype(data_0_2)
assert shape == F.shape(data_0_2)
assert policy.policy_str == 'node'
meta = kvclient.get_data_meta('data_0_3')
dtype, shape, policy = meta
assert dtype == F.dtype(data_0_3)
assert shape == F.shape(data_0_3)
assert policy.policy_str == 'node'
meta = kvclient.get_data_meta('data_1')
dtype, shape, policy = meta
assert dtype == F.dtype(data_1)
assert shape == F.shape(data_1)
assert policy.policy_str == 'edge'
meta = kvclient.get_data_meta('data_2')
dtype, shape, policy = meta
assert dtype == F.dtype(data_2)
assert shape == F.shape(data_2)
assert policy.policy_str == 'node'
# Test push and pull
id_tensor = F.tensor([0,2,4], F.int64)
data_tensor = F.tensor([[6.,6.],[6.,6.],[6.,6.]], F.float32)
kvclient.push(name='data_0',
id_tensor=id_tensor,
data_tensor=data_tensor)
kvclient.push(name='data_1',
id_tensor=id_tensor,
data_tensor=data_tensor)
kvclient.push(name='data_2',
id_tensor=id_tensor,
data_tensor=data_tensor)
res = kvclient.pull(name='data_0', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
res = kvclient.pull(name='data_1', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
res = kvclient.pull(name='data_2', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
# Register new push handler
kvclient.register_push_handler(udf_push)
# Test push and pull
kvclient.push(name='data_0',
id_tensor=id_tensor,
data_tensor=data_tensor)
kvclient.push(name='data_1',
id_tensor=id_tensor,
data_tensor=data_tensor)
kvclient.push(name='data_2',
id_tensor=id_tensor,
data_tensor=data_tensor)
data_tensor = data_tensor * data_tensor
res = kvclient.pull(name='data_0', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
res = kvclient.pull(name='data_1', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
res = kvclient.pull(name='data_2', id_tensor=id_tensor)
assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
# clean up
dgl.distributed.shutdown_servers()
dgl.distributed.finalize_client()
@unittest.skipIf(os.name == 'nt' or os.getenv('DGLBACKEND') == 'tensorflow', reason='Do not support windows and TF yet')
def test_kv_store():
ip_config = open("kv_ip_config.txt", "w")
ip_addr = get_local_usable_addr()
ip_config.write('%s 1\n' % ip_addr)
ip_config.close()
ctx = mp.get_context('spawn')
pserver = ctx.Process(target=start_server)
pclient = ctx.Process(target=start_client)
pserver.start()
time.sleep(1)
pclient.start()
pserver.join()
pclient.join()
if __name__ == '__main__':
test_partition_policy()
test_kv_store()